ALLEN N. BERGER
ASTRID A. DICK
Entry into Banking Markets and the Early-Mover
Advantage
Using a sample for 1972–2002 with over 10,000 bank entries into local
markets, we find a market share advantage for early entrants. In particular,
the earlier a bank enters, the larger is its market share relative to other banks,
controlling for firm, market, and time effects, with a market share advantage
for early movers between 1 and 15 percentage points, depending on the order
of entry. The strongest early-mover advantage is for banks that were in our
sample in 1972 and survive into the 1990s. Moreover, early entrants appear
to have such hold in the market by strategically investing in larger branch
networks. Even controlling for the potential survivorship bias, we find that a
bank’s share decreases by 0.1 percentage points for a change in its order of
entry from nth to (n + 1)th. High growth markets show a smaller difference
between late and early movers, consistent with a larger fraction of consumers
yet to be locked in with a bank in these markets.
JEL codes: G2, L1
Keywords: banks, market entry, market structure, firm strategy, first-mover
advantage.
THE ADVANTAGES OF early entry, such as a first-mover advantage, are frequently mentioned in both the economics and the business literature, yet
the empirical research accompanying the theoretical developments since Stackelberg
The opinions expressed do not necessarily reflect those of the Federal Reserve System. The authors
would like to thank the editor, two anonymous referees, Adam Ashcraft, Nicola Cetorelli, Manfred Dix,
Bob Hunt, George Kaufman, Matthew Shum, and James Vickery for insightful comments, as well as
participants at the Federal Reserve Bank of Chicago Bank Structure and Competition Conference, the
International Industrial Organization Conference, the Financial Management Association Conference, and
a Bank of Canada seminar. Nathan Miller and Philip Ostromogolsky provided excellent research assistance.
All remaining errors are the responsibility of the authors.
ALLEN N. BERGER is at the Board of Governors of the Federal Reserve System,
Washington, D.C., and at Wharton Financial Institutions Center, Philadelphia (E-mail:
[email protected]). ASTRID A. DICK is at INSEAD Business School, Fontainebleau, France
(E-mail: [email protected]).
Received November 16, 2004; and accepted in revised form February 14, 2006.
Journal of Money, Credit and Banking, Vol. 39, No. 4 (June 2007)
C 2007 The Ohio State University.
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(1934) has been limited. An early-mover advantage might arise under certain elements that create obstacles to subsequent entry and allow incumbents to earn rents
even when entry occurs. These elements include certain capital investments, such as
building a clientele when switching costs are present, learning by doing, or economies
of scale.
The extant empirical literature exploring the relationship between the order of
entry and a firm’s market presence and performance has focussed on differentiated
products where innovation is central to product development. In contrast, this paper
focuses on a service industry. In particular, within the context of the banking industry,
this paper investigates whether early movers have an advantage over later movers by
exploring whether the order of entry is related to the degree of market dominance.
Various factors could give rise to such an advantage in banking. Both the anecdotal and
empirical evidence suggest that consumer switching costs are significant in banking,
with some evidence on supply-side factors such as economies of scale as well. The
banking industry provides a unique opportunity to study such a phenomenon, with the
availability of decades of data that allow us to determine the date of entry and exit of
thousands of firms into hundreds of local markets, with information on their market
shares, as well as other firm characteristics. Moreover, the data provide substantial
variation in dates of entry into a given market, as it is common in banking markets
to find banks that entered several decades ago coexist with banks that entered only
recently.
While there is no direct research evidence indicating an early-mover advantage in
banking, an abundance of anecdotal evidence suggests that, at least in the short run
and for large banks, a dynamic disadvantage exists for later entrants. In particular,
established banks that expand into new markets appear to perform worse than their
competitors, losing deposits, loans, and profits. 1 Some of the reasons put forth for this
poor performance include lack of personal contact and loss of personal relationship
for the client, “cookie cutter” products that are not tailored to the individual customer’s
needs, and the banker’s lack of knowledge about the local community. Conversely,
incumbent banks tend to accumulate proprietary information about their customers
and their local communities. Indeed, an early-mover advantage may be more likely
in banking than in other industries due to the importance of relationships.
In order to illustrate the advantage of an incumbent, we use a variation of the von
Stackelberg two-stage, two-firm model based on Spence (1977) and Dixit (1979),
which reinterpret the basic model as a reduced-form derived from short-run product–
market competition under capacity constraints. While our analytical framework is
quite simple, it embodies the main aspects of sequential entry under sunk costs.
In particular, it illustrates how early movers can make their investment decisions
strategically and fare better than later movers, by “pre-empting” the market through
1. Bank mergers are associated with runoffs of deposits and loans. DiSalvo (2002) finds that mergers
often result in negative deposit growth for the consolidated institutions or smaller growth than that of
non-merging competitors. Large banks also tend to have reductions in their small business lending in the
aftermath of mergers and acquisitions that is picked up by their local market competitors (e.g., Berger
et al. 1998).
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a higher capital investment, thereby limiting the erosion of rent due to entry. In
this model, the incumbent or early mover, who decides how much capital to invest by taking into account how the entrant will react to its choice of capital,
ends up with higher capital investment and subsequent profits than the entrant.
This captures the incumbent’s early-mover advantage. In our setup, capital investment is interpreted, without loss of generality, as building a clientele, with incumbents developing larger clienteles than entrants. In terms of the banking data, the
model implies that incumbents should enjoy larger deposit market shares than later
entrants.
Our unique data set, which covers the period 1972–2002 and over 10,000 bank
entries, allows us to identify the entry and exit date (if any) of all banking firms in
urban markets—defined at the level of the metropolitan statistical area—that occurred
throughout the three decades, as well as the incumbent firms as of our starting date
in 1972. At each point of time, we divide banks into four groups, based on how long
ago they have entered the market, ranging from the last 5 years to over 20 years ago.
The regression analysis focuses on the period 1992–2002, in order to have banks fall
into each category. The data include 318 markets and more than 8,000 bank entries
(of the 10,655 entries within 1972–2002, 5,381 occur within 1992–2002; of the other
5,274 occurring within 1972–91, 3,115 of them survive into the 1990s).
A test of an early-mover advantage is useful for many reasons. First, it may help
in understanding the type of strategic competition that takes place in the industry.
Second, measuring the magnitude of any early-mover advantage is a way to gauge
how significant barriers to entry are in the industry, indirectly measuring the factors
giving rise to them, such as switching costs and other consumer transaction costs on
the demand side, and cost advantages from learning by doing, decreasing average
costs and capital accumulation on the supply side. Moreover, banking markets make
a great laboratory for the study of an early-mover advantage. Unlike some previous
work, which relies on surveys, polls, and various assumptions in order to distinguish
pioneer firms, identifying incumbents and later entrants is straightforward in the case
of banking markets given the nature of bank entry and the available banking data.
In addition, the relevant geographic market in the banking industry, which tends to
be local and defined at the metropolitan level, provides a wealth of cross-market
variation.
Our results show that banks that enter markets early enjoy larger deposit market
shares, after controlling for firm, market, and time effects. The later a bank enters,
the smaller is its market share relative to early entrants. While all early entrants have
an advantage over later entrants, it is particularly the 1972 incumbents (those firms
present at the beginning of our sample that survive into the 1990s) that have the largest
market share advantage. While entrants face a disadvantage, they can ameliorate such
an effect if they enter by merger as opposed to opening a branch or through a de
novo charter. Similarly, geographically diversified entrants face a lower disadvantage relative to more locally limited entrants. Moreover, we find that early entrants
appear to have such hold in the market by strategically investing in larger branch
networks.
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In addition, we are able to distinguish the early-mover advantage from alternative
stories. In particular, a learning model, where firms face a self-reinforcing productivity
shock every period and discover their type over time, implies an equilibrium where
the size distribution of firms is increasing in the age of firm cohorts. Similarly, under
a scenario of imperfect capital markets, such that most of the firm’s ability to invest
and therefore grow are derived from internally generated funds, the firm’s assets will
be correlated with the firm’s age. In order to rule out these alternative stories, we
explore whether earlier entrants have larger shares of the market compared to later
entrants, after surviving in the market for the same number of years. That is, we
explicitly account for the order in which entry has occurred by holding the number
of years since entry constant across all banks, and therefore address the potential
survivorship bias in our sample. Even when we control for the number of years in the
market, we find that a bank’s share decreases by 0.1 percentage points for a change
in its order of entry from nth to (n + 1)th. Moreover, each additional year in the
market increases a bank’s deposit market share by 0.01 percentage points, regardless
of the order in which the bank entered the market. Thus, this test is particularly
powerful as it allows us to separate the effects of market tenure from those of the
order of entry. The test indicates that while market tenure increases a bank’s market
share, the later a bank enters a market, the lower its market share relative to early
entrants.
We also offer another test for whether our earlier results are related to a strategic
advantage of early entry. If access to capital markets and/or firm learning are the
main factors determining a bank’s entry and growth in a market, we should find that
multi-market banks that are incumbents in some markets but entrants in others do not
depict large differences in market shares across these markets. If the bank enters a
market and wants to grow to the extent of the incumbent, it should be able to do so.
However, this is not what we find. On the contrary, larger, multi-market banks do not
achieve in new markets the large market shares that they have in markets where they
are incumbents. That is, even these bank entrants do worse relative to incumbents.
A bank with “deep pockets” can enter a market and build a large branch network to
drive out smaller banks if it wants to—that is, if it believes it would be profitable
to do so as it would be able to attract other banks’ customers. The evidence in this
paper shows that there is a fraction of consumers that will stay with the incumbent
regardless, and that is why even a large bank with access to capital and accumulated
knowledge might not become as large in markets where it enters later, and branch out
in these as much. In fact, compared to the overall sample, the results indicate that the
difference between these banks’ market shares when they are entrants and those when
they reach maturity in the market are larger. This suggests that larger, multi-market
entrants grow faster than smaller, single-market entrants.
Moreover, we find that high-growth markets show a smaller difference between late
and early movers. These results support the prediction from consumer switching costs
models that higher population growth markets should exhibit less of an early-mover
advantage. Indeed, they suggest that consumer switching costs are likely an important
factor behind the documented market share difference. In low-growth markets, the
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number of new consumers is low, and therefore entrants should face greater barriers
to their market growth.
There are at least two important policy implications of the paper’s main finding that
entrants cannot compete on an equal footing with incumbents. The first is that a large
entrant might not signify as much of a threat to an established community bank. This
may help explain why so many thousands of community banks survive when many
had predicted their demise—the large banks merging across the nation cannot enter
their markets and just take their local market shares away from them. Second, for
antitrust analysis, potential entry might not be as effective as a competitive force in
a market, especially one with low-population turnover. Thus, regulators, who define
potential entry as a “mitigating factor” for the possible anti-competitive effects of a
merger, should adjust for the fact that entrants may not be able to fully compete head
to head with incumbents. We discuss both of these implications in the paper.
The paper is organized as follows. Section 1 briefly reviews the literature on the
advantage of early movers, while Section 2 provides a discussion of how such an
advantage might arise in banking as well as the prior empirical evidence on this issue.
Section 3 describes the empirical framework. Section 4 presents our empirical results,
and Section 5 concludes.
1. EARLY-MOVER ADVANTAGE: THEORY AND EVIDENCE
An early-mover advantage might arise under various demand and supply conditions. 2 On the demand side, factors such as switching costs, network externalities, and
buyer inertia due to quality uncertainty and/or habit formation can result in an early
mover having an advantage over later entrants. The supply-related factors include
setup and sunk costs, scale economies, supply chain, and learning by doing.
All of these factors have been studied under various setups. 3 However, the sequential incumbent entrant games that explicitly model the first-mover advantage are few
in the literature. One insightful paper in this respect is that by Schmalensee (1982)
who studies the long-lived advantages to pioneering brands when buyers face imperfect information about product quality. In a two-period model, two brands enter
sequentially, and while being identical, the second brand has a disadvantage because
customers have already tried the first brand in the first period and found that it is
satisfactory.
Within the economics literature, empirical work on the advantage of early movers,
which has mostly been interpreted as a market share advantage, has focused on the
pharmaceutical industry (Grabowski and Vernon 1992, Hurwitz and Caves 1988,
Gorecki 1986), with some other applications such as those to financial innovation
(Tufano 1989) and Internet search engines (Gandal 2001). Grabowski and Vernon
(1992) analyze 18 drugs experiencing generic competition upon patent expiration by
2. Muller (1997) provides a study of several elements that allow a first-mover advantage to exist.
3. For example, Klemperer (1987) analyzes switching costs in a two-period duopoly model where two
firms are always in the market.
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exploring price and market share patterns of incumbents and entrants, generally finding, as the earlier literature, that pioneers maintain premium price positions after entry
while their market shares erode over time. Tufano (1989) finds a strong relationship
between product innovation and investment banks’ market shares using a database of
58 financial innovations. Gandal (2001), based on a period of 1 year, uses a discrete
choice logit model to find that early entrants in the Internet search engine market
enjoy a consumer utility premium.
There is a significant body of evidence from marketing research on pioneer and
early-entrant advantages for non-financial firms. This work, however, is based on
surveys or data that are restrictive and present problems related to the levels of industry aggregation and distinguishing the order of entry, among others. 4 Examples are
Robinson and Fornell (1985) on consumer goods and Robinson (1988) for industrial
goods.
2. THE EARLY-MOVER ADVANTAGE AND THE BANKING INDUSTRY
In the banking industry, demand-driven factors are likely to be important in giving
rise to an early-mover advantage. 5 Both the anecdotal and empirical evidence suggest
that consumer switching costs are significant. The evidence on supply-side factors,
such as economies of scale, is mixed, showing both economies and diseconomies of
scale in bank products.
Switching costs appear to be prevalent in the use of banking services. 6 Anecdotal
evidence suggests that depositors find it costly to close an account with their current
bank to open an account in another bank. Time is invested in doing so, funds may
be tied in the process, and the new service might require some specific investment in
learning to use it. Other switching costs might include uncertainty over the quality
of service, such as branch service quality, product availability, and even how long it
takes to get through the phone system to a customer representative. 7 Customer inertia
is likely to be such that in order for a consumer to switch banks, at least one of the
following should occur: current service deteriorates relative to expected new service at
another bank and this deterioration is enough to cover switching costs; large discount
offered by another bank; some other large expected gain from switching (e.g., new
4. The data are usually from the profit impact of market strategy. See Kerin, Varadarajan, and Peterson
(1992), Szymanski, Troy, and Bharadwaj (1995), Kalyaran, Robinson, and Urban (1995), and VanderWerf
and Mahon (1997) for a survey and discussion of the problems of this literature.
5. Note that, in principle, we are agnostic about whether there is an early-mover advantage or disadvantage. The latter might arise from conditions such as being a large banking organization in the form
described by Williamson (1967); managers pursuing the quiet life by choosing to grow through in-market
mergers, a relatively simple strategy (Muller 1997); as well as other diseconomies. We focus on the former
since this is suggested by the anecdotal evidence and our results.
6. A search on American Banker evidences how the notion of switching costs is very much at the
forefront of banking strategies.
7. Zephirin (1995) builds a model for the deposit market where consumers care about both price and
service quality but the latter is uncertain, giving rise to switching costs that grow proportionally to the
length of the bank–customer relationship.
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technology that allows new products that the current bank is not offering yet, such as
Internet access).
The empirical evidence suggests that customers stay with the same bank for a long
time largely due to the cost of switching banks. Based on survey data, Kiser (2002a)
finds that the average household stays with the same bank for 10 years, while the most
frequently cited motivation for changing banks is a household relocation. Moreover,
she finds that a third of households report that a main reason why they have stayed with
their bank is the inconvenience of changing institutions. Using the same data, Kiser
(2002b) finds that owning a home, interpreted as geographic stability, is the leading
cause for consumers staying with the same bank for a long period. Kiser also finds
that switching costs, as reported by the consumer, appear to be most severe for high
income, high education, very low income, and ethnic-minority households, suggesting
that banking markets where these demographic characteristics are prevalent may
present greater costs of entry for new banks. Sharpe (1997), using a model based on
Klemperer (1987), tests the prediction that under consumer switching costs, markets
with higher population turnover and/or growth should be more competitive, and finds
that migration has a positive effect on deposit interest rates. Calem and Mester (1995),
in a careful analysis of consumer survey data, find that much of the credit card interest
rates stickiness is related to consumers facing switching costs across credit card
issuers and the resulting exercise of market power by the firms. Stango (2002) also
finds a strong relationship between various measures of switching costs and credit
card interest rate pricing by commercial banks. Finally, Kim, Kliger, and Vale (2003)
estimate a firm model where consumers face switching costs, and, applying it to bank
loan data, find that switching costs play a significant role in the lending market. In
particular, they find that switching costs represent about a third of the interest paid on
average by loan customers, and are responsible for about a third of the market share
of the average bank.
Under switching costs, firms enjoy market power over their repeat purchasers.
As a result, a firm’s current market share is an important determinant of its future
profits (Klemperer 1995). Each period, a firm faces a trade-off between decreasing
its price to increase market share through new customers, and exploiting its current
customer base through higher prices at the cost of a reduced market share. What
the firm chooses to do depends on supply factors such as the threat of new entry as
well as demand factors such as market growth in population and income. If buyers
face imperfect information and/or firm entry occurs sequentially, building a clientele
becomes a strategic decision of the firm. Indeed, building a customer base appears to
be a major objective of banks. Dick (2007) finds that banks with the largest market
shares are also the ones with the largest branch networks and the heaviest advertisers.
Linking profitability and market concentration to advertising intensity, Örs (2003)
finds that advertising plays a role in banking competition as advertising appears to
increase profitability, especially in less concentrated markets.
Moreover, building a clientele can increase the barriers to entry through its relationship to firm access to capital. A bank that acquires a customer base in a given market
has access not only to the customers’ needs for banking services, but importantly, to
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the customer’s funds, which reinforces an incumbent’s clientele as a barrier to entry
to outside firms.
Furthermore, the importance of relationships, as an entire body of literature suggests, 8 makes an early-mover advantage more likely to occur in banking than in other
industries, by giving rise to both customer switching costs and asymmetric information. Over time, incumbent banks accumulate private information about the loan
customers, depositors, and the local community, and as a result later entrants can be
at a disadvantage due to adverse selection problems. In this last respect, Dell’Ariccia,
Friedman, and Marquez (1999) and Marquez (2002) provide banking models where
access to information might create difficulties for potential entrants, as banks obtain
much information about the creditworthiness of their borrowers after lending to them.
On the supply side, scale economies, a potential factor giving rise to an early-mover
advantage, appear to be of importance for some bank types and products, but not for
others, such that the evidence is somewhat mixed. On the one hand, based on extensive
empirical work, economies of scale appear to exist for small-sized banks, while
larger banks experience scale diseconomies (Berger and Mester 1997). Moreover,
while some bank products might enjoy increasing returns to scale, such as electronic
payments systems, management of a bank might run into decreasing returns as firms
grow into larger institutions. This is also suggested in the work of Williamson (1967),
as large firms might become more bureaucratic and have difficulties processing their
large amounts of information flows. 9 On the other hand, Hughes et al. (2000) argue
that standard techniques to estimate scale economies do not appropriately account for
the interplay between bank capital, risk, and managerial preferences in the production
function of the bank, and find larger scale economies following this adjustment.
3. EMPIRICAL FRAMEWORK
3.1 Analytical Framework
Entry of new firms is a phenomenon widely observed in banking markets. Banks
differ greatly in terms of when and how they enter the market and how long they remain
in it. An early-mover advantage can arise under the existence of certain conditions that
create a barrier to entry, which affect the ability of incumbents to prevent the erosion of
rent from entry. These elements, which can therefore result in an early mover having
an advantage over later entrants, range from switching costs, on the demand side, to
setup costs and scale economies, on the supply side. However different, all of these
factors have a common nature, as they fall within the general category of fixed-cost
investment decisions of the firm.
In the case of the banking firm, the process of building a clientele can be seen as
a form of capital investment, and as such, a barrier to entry for later movers. To earn
8. There is an extensive literature that documents the focus of smaller banks on lending to small firms
or so-called relationship lending. See Berger et al. (2005) and Cole, Goldberg, and White (2004).
9. At certain levels of size, in fact, this could be the source of a first-mover disadvantage (Muller 1997).
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these captive consumers the firm must build branches and invest in advertising and
other promotional activities. The costs involved in building this market share are, at
least in part, fixed and sunk. They are fixed in the sense that the cost of building
the branch/advertising does not vary with the number of consumers served/reached
by the branch or ad; and they are sunk in that all or a large part of branch building
costs/advertising cannot be recouped. These consumers, by becoming a bank’s clients,
now face costs of switching to another bank. Thus, when a new bank enters, it does
not compete on a level playing field with the incumbent. Such barrier to entry, even
when it does not prevent entry, makes it more difficult, and could allow incumbent
firms to enjoy some rents. This is reinforced by how the bank’s client base affects the
bank’s access to local funds.
Whenever strategic interaction is part of the competition among firms, in that a
firm’s behavior can affect another firm’s behavior, one would expect incumbents who
face an entry threat to take action to either deter entry or accommodate it, whichever
is more profitable. In order to capture the advantage of an incumbent, we use as an
illustration a variation of the von Stackelberg two-stage, two-firm model based on
the Spence and Dixit re-interpretations (Spence 1977, Dixit 1979). In particular, the
Stackelberg quantity can be redefined as capacity, which is interpreted here, without
loss of generality, as the bank’s clientele, while the profit functions are rationalized
as reduced-form functions resulting from a price game under capacity constraints. In
the second stage, we have a price game under capacity constraints, where both firms
basically dump their capacities in the market, in a manner analogous to Cournot—
though, instead of an auctioneer, it is the firms that quote the market price at which
demand equals aggregate capacity, as in Kreps and Scheinkman (1983). In the first
stage, firms choose capacities sequentially. The incumbent or early mover decides
how much capital to invest by taking into account how the entrant will react to its
choice of capital. From the Stackelberg model we know that the incumbent’s capital
investment and subsequent profits are higher than those of the entrant’s. This captures
the incumbent’s early-mover advantage.
While this model is quite simple, it actually embodies some of the main aspects of
sequential entry under sunk costs. The focus of the model on market share benefits,
which is what is usually referred to as the first-mover advantage, is appropriate in
light of the theoretical developments related to entry barriers, as well as the empirical evidence on the link between market share and profitability. Under barriers to
entry, market share is usually linked to performance in a direct way. For instance,
under switching costs, the firm has a degree of monopoly power over its captive
consumers and therefore current market share is an important determinant of future
profits (Klemperer 1995). Schmalensee (1989), in a Handbook of Industrial Organization article, lists the association between market share and firm profitability to be
among the main systematic relationships documented in the inter-industry studies.
Muller (1986), in a lengthy study of long-run profitability, finds that profit differences
across manufacturing companies are strongly related to market shares. In the banking
literature, the link between profits and market share has not received a lot of attention, but when it has been studied, the relationship came in positive. In particular,
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Berger (1995) provides support for the relative market power hypothesis that larger
shares are associated with greater exercise of market power and higher profits (after
controlling for concentration and efficiency). At best, however, we recognize that
market share, which is the focus of our analysis, is an imperfect proxy for profits.
Intuitively, the model captures the chance of an early bank entrant to obtain a
significant portion of the population of a market. These captive consumers open an
account with the incumbent, learn whatever specificities there are related to the bank
services, and become accustomed to their bank. When another firm enters the market,
it has to build a clientele from the non-captive consumer base.
More realistically, when entry occurs the incumbent will usually face two types
of consumers: those already captive and the rest of the consumers in the market,
which are up-for-grabs and over whom it is fighting with the entrant. If able to
price discriminate, the incumbent would like to charge a high price to its captive
consumers, over whom it exerts a large degree of market power, 10 and a lower price
to the other consumers in order to attract their business. Under no price discrimination,
the firm will have to choose an intermediate price, which will be increasing in the
size of the captive clientele. If the firm invests enough in building this clientele, or
“overinvests” in the terminology of Fudenberg and Tirole (1984), the price will be high
enough to make entry profitable and to “soften” price competition. In particular, the
incumbent would be a “fat cat” that accommodates entry, according to the Fudenberg
and Tirole taxonomy, such that the investment in clientele makes the incumbent
soft under strategic complements (price competition in the market stage), where an
increase in the incumbent’s capacity investment leads to an increase in the entrant’s
profits. The incumbent’s investment, while reducing the size of the market for the
entrant, also increases the price that both of them can charge.
3.2 Empirical Specification
In the Stackelberg model, the early-mover advantage is reflected in the firms’
market shares, which are central to the firms’ future profits. This is what drives our
empirical specification, which is designed to investigate whether early movers have an
advantage over later movers by exploring whether there is any relationship between
a firm’s order of entry into a market and the size of the firm’s market share.
To explore whether early movers are distinctly different from later movers in terms
of market presence, we define a set of time ranges for the order of entry into a
market. A bank is usually believed to have reached maturity after around 20 years
in a market. 11 Letting T be the year in which a bank enters a given market, a firm at
time t is categorized based on its order of entry into the market, that is, according to
whether (1 = yes, 0 = no):
10. In markets with significant population flows, the incumbent’s advantage should be less (see Beggs
and Klemperer 1992).
11. For instance, several papers have found that small banks initially have very high ratios of business
loans to assets, and this does not fully decline to equilibrium until about age 20 (e.g., Goldberg and White
1998, DeYoung, Goldberg, and White 1999).
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(i) the bank entered the market within the last 4 years, such that 0 ≤ T < 5 years;
(ii) the bank entered the market between 5 and 9 years ago, such that 5 ≤ T < 10
years;
(iii) the bank entered the market between 10 and 19 years ago, such that 10 ≤ T <
20 years.
This leaves out those firms that entered the market 20 years ago or earlier (T ≥ 20).
We refer to this latter group as the early entrants or “incumbents,” while referring to the rest of banks as entrants. Note that in the specification, we distinguish between “1972 incumbents,” which are banks that were already in the
market in 1972 (the beginning of our sample), and the incumbents that came
in later. The latter represent the base case for comparison. While we have data
since 1972, the regression analysis focuses on the period 1992–2002, in order to
have banks fall into each category. Based on these indicators, we define market
share of bank i in market m at time t to be a function of the order of entry as
follows:
j
Market Sharei,m,t =
β j ∗ Order of Entryi,m,t
j
(1)
+ I N 1972 + γm + ηi + τt + νi,m,t ,
j
where j stands for the three order of entry periods, with the variable Order of Entryi,m,t
taking on the value of one if the condition applies, and zero otherwise; I N 1972 takes
on the value of 1 if the bank was already in the market in 1972; γm is a market fixed
effect; ηi is a bank specific effect; τt is a year effect; and νi,m,t is a random disturbance.
The fixed components for market, bank, and year are included in order to control for
those factors that might affect market shares other than the order of entry and that
could be correlated with the latter.
We also investigate whether the entry method into the market has an effect on
market share given the order of entry, such that our specification becomes
Market Sharei,m,t =
j
β j ∗ Order of Entryi,m,t
j
j
k
+
β jk ∗ Order of Entryi,m,t ∗ Entry Methodi,m
j
k
+ I N 1972 + γm + ηi + τt + νi,m,t ,
(2)
where k stands for the three methods of entry, as discussed later: (i) merger, (ii) de
novo, and (iii) opening a branch. This is the structure that we will use to explore
the effects on market share from another important firm characteristic, namely, its
geographic diversification. In particular, we divide banks into two groups: one with
presence in more than 10 metropolitan markets, and another with presence in 10 or
fewer metropolitan markets. This bank characteristic is usually correlated with bank
size as well as brand recognition.
786
:
MONEY, CREDIT AND BANKING
3.3 Data
The data used in the analysis are taken from the Federal Deposit Insurance Corporation Summary of Deposits. 12 Given our sample for the period 1972–2002, we
use branch deposit data since 1972 to determine the deposit market share, as well
as the entry and exit date, if any, for each firm into each market. To obtain a bank’s
deposit market share, we add deposits in all of the bank’s branches in a given market.
Based on this definition, our regression analysis covers the period 1992–2002. Note
that while one might also be interested in analyzing other bank output, such as loans,
data at the branch level are only available for deposits. Similarly, it would also be
interesting to explore an early-mover advantage in terms of profits, prices, and risk.
However, these data are only available for the bank as a whole, and not for each
bank-market observation. At best, market share, which is the focus of our analysis,
can be interpreted as a proxy for profits. 13 Also, our deposit data aggregate all types
of deposit accounts. While most of these are local in nature and involve the consumer
switching costs discussed earlier, some (such as negotiable CDs) have a national market and are likely to involve lower switching costs. However, the evidence suggests
that consumers tend to buy deposit products locally and to cluster them with a single
institution (Amel and Starr-McCluer 2002).
While determining the entry date is sometimes controversial for other industries,
the banking data allow us to identify entrants in a straightforward fashion. We are
also able to determine the method of entry chosen by the firm. In particular, we can
distinguish among the following three types of entry into a market: (i) merger with
a target-market bank (absorbing the charter), (ii) de novo (new banks), (iii) opening
a branch (outside market bank). 14 Note that a banking holding company can enter a
market by buying a target-market bank through an acquisition. This changes the bank
ownership but does not alter the bank charter, such that from the perspective of the
consumer and the regulatory agency the banking institution is still the same. Thus,
we do not consider these acquisitions as entry. 15
While deposit data are available for each bank branch in the United States, we define
the relevant geographic banking market at the level of the metropolitan statistical area
(MSA), which represents a geographic unit with a large population nucleus with its
12. We use data from the Report on Condition and Income from the Federal Reserve Board for some
of the descriptive statistics, and data from the National Information Center to determine the method of
entry.
13. In the Results section, we briefly explore the link between the order of entry and profit rates for
single-market banks, for whom profit data at the market level are obviously available.
14. This last category includes the case of a bank entering a market by purchasing branches of another
bank.
15. Note that when a bank is acquired without a change in its bank charter, it becomes affiliated with a
Multi-Bank Holding Company (MBHC). MBHCs might serve as internal capital markets for their member
institutions (Houston, James, and Marcus 1997, Houston and James 1998, Campello 2002). Thus, these
acquisitions that provide MBHC membership without changing the bank charter might alter the target’s
behavior if they provide the target with access to an internal capital market. To deal with this issue, as a
robustness exercise we control in our regressions for whether the bank is subjected to an acquisition that
preserves its charter. We find that our results are robust to the inclusion of such control, as will be discussed
later.
ALLEN N. BERGER AND ASTRID A. DICK
:
787
adjacent communities (a city or town). 16 This definition is supported by surveys of
consumers and businesses as well as the bulk of the empirical banking literature. 17
There are 318 MSA markets in our data, which include almost all metropolitan markets
in the United States (over 96%, based on the 1999 census definition).
The appendix contains the summary statistics for the variables used in the analysis.
Based on our regression sample of surviving banks for the period 1992–2002, there
are 8,496 bank entries into local markets since 1972. 18 Table 1 presents the number
of banks by year and number of years in the market (under “Time” ), based on bankmarket observations across time. 19 The date of entry into a market can be established
up to 1972, given that our sample starts that year. We use these data to categorize firms
by the order of entry. The numbers in italics represent banks that entered in 1972 or
earlier and have survived since then (and for whom the date of entry into the market
cannot be determined), while the rest of the numbers of the table are bank entries.
The first line on the table, where Time = 0, corresponds to the new entrants each year
of our analysis. Thus, there was an average of 489 entries into local markets per year,
with a larger number of entries towards the second part of the period (275 entries in
1992 versus 517 in 2002). The total number of entries throughout 1992–2002 is 5,381,
as indicated in the last column. The second line, where Time = 1, corresponds to the
surviving banks that entered the previous year—1-year-old banks—and so forth for
the following lines of the table. For instance, under the 2002 column, the 105 banks
in the row where Time = 10 corresponds to those firms that entered 10 years ago, or,
in other words, have been in the market for 10 years. Therefore, of the 275 banks that
entered in 1992, only 105 of them remain in 2002.
Table 2 shows the distribution of banks across markets in terms of their order of
entry in each year of the analysis. While in the earlier part of the sample almost half
of the firms in the market are old (with market presence of over 20 years), toward the
second half most market banks are new entrants.
Table 3 depicts the average deposit market shares by time in the market. One fact
that comes out of the table is the stark difference in market shares between entrants
and banks that have been in the market for 20 or more years. While entrants average
16. We focus on metropolitan markets rather than rural markets primarily because these are the markets
that are generally considered to be more competitive with easier entry/exit and acquisition of local market
shares. Finding an early-mover advantage in these markets would therefore be more surprising to us. Since
this is the first analysis done of this kind, we also think it is better to focus on metropolitan markets in
order to learn about whether there exists an early-mover advantage in banking, given that these banks have
over 80% of industry deposits. However, the study of rural markets is an interesting direction for future
research, given that the interaction among banks in rural markets is likely to be systematically different.
17. For a detailed discussion on the relevant geographic market definition in banking, see Dick (2002)
and the references therein.
18. Note that our sample includes 10,655 entries over 1972–2002. Of those, 5,381 entries occur during
1992–2002, while the other 5,274 entries occur throughout 1972–91. Out of these 5,274 entries, however,
only 3,115 entrants survive into the 1990s. That is how we come up with the number of 8,496 entries since
1972 for the sample 1992–2002.
19. Note that our original data set included some bank observations that revealed multiple entries into
the same market throughout our sample period. This happens in the uncommon situation where a bank
enters a market, stays for a few years, then leaves the market, and returns again after a few years. These
observations constituted only around 1% of the observations, and we have dropped them from our sample.
788
:
MONEY, CREDIT AND BANKING
TABLE 1
NUMBER OF BANKS BY YEAR AND NUMBER OF YEARS IN THE MARKET
Year
Time
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Total
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Total
275
273
275
298
253
209
194
231
204
174
129
111
102
79
58
74
86
108
107
93
3,292
.
.
.
.
.
.
.
.
.
.
6,625
222
262
254
248
276
237
186
172
210
187
155
121
102
96
72
56
65
80
103
100
87
3,105
.
.
.
.
.
.
.
.
.
6,396
241
200
250
237
231
260
227
171
158
196
159
143
117
91
89
65
55
58
75
97
93
80
2,915
.
.
.
.
.
.
.
.
6,208
356
226
193
226
226
219
241
204
158
149
182
143
128
107
87
86
58
50
53
71
88
85
74
2,707
.
.
.
.
.
.
.
6,117
438
332
208
180
204
210
205
212
189
147
130
156
125
116
98
79
78
48
48
46
64
81
79
68
2,452
.
.
.
.
.
.
5,993
706
378
299
180
154
166
186
177
194
154
129
111
146
118
102
82
67
72
45
44
39
56
78
71
58
2,241
.
.
.
.
.
6,053
696
631
351
263
160
135
152
172
164
161
139
117
100
124
103
88
76
65
64
36
38
35
49
72
68
56
2,078
.
.
.
.
6,193
683
635
579
296
228
137
118
134
151
147
140
126
102
84
110
86
78
67
56
59
31
32
32
47
64
56
52
1,926
.
.
.
6,256
614
652
598
526
274
207
129
112
125
140
133
124
113
94
81
103
77
65
65
51
50
28
30
31
44
55
55
49
1,792
.
.
6,417
633
596
597
565
497
240
192
118
104
118
123
121
96
103
87
71
97
71
58
62
46
44
28
27
31
43
53
49
47
1,675
.
6,592
517
575
556
549
504
444
226
179
110
100
105
110
112
90
96
73
65
90
63
55
56
42
43
28
24
30
41
50
40
42
1,596
6,611
5,381
4,760
4,160
3,568
3,007
2,464
2,056
1,882
1,767
1,673
1,524
1,383
1,243
1,102
983
863
802
774
737
714
3,884
3,588
3,328
3,051
2,741
2,481
2,279
2,074
1,879
1,717
1,596
69,461
NOTES: Based on bank-market observations. Dates of entry into a market are based on a sample for 1972–2002. Numbers in italics represent
banks that entered in 1972 or earlier and have survived since then (and for whom date of entry into the market cannot be determined), while
the rest of the numbers of the table are bank entries. The first line on the table, where Time = 0, corresponds to the new entrants each year of
our analysis. The second line, where Time = 1, corresponds to the surviving banks that entered the previous year—1-year-old banks— and
so forth for the following lines of the table.
a market share of 3.3%, incumbents have more than double this market share, with
an average of 7.1.
Entry methods are distributed more or less equally, though the most popular method
of entry is de novo, with 37% of the total entries in our sample, followed by 33%
of the entries through branch opening and the rest using a merger. Table 4 provides
information on the entrants’ average market shares based on the method of entry used:
not surprisingly, banks that enter through mergers have on average the largest market
shares, relative to entering via de novo or opening a branch. Another feature from the
table is that market shares usually rise on average as a bank accumulates years in a
market (note that the figures for the oldest entrants via merger are based on a small
number of observations).
ALLEN N. BERGER AND ASTRID A. DICK
:
789
TABLE 2
AVERAGE MARKET DISTRIBUTION OF FIRMS BASED ON THE ORDER OF ENTRY
Year
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Entrant
0≤t <5
Entrant
5 ≤ t < 10
Entrant
10 ≤ t < 20
Entrant
20 ≤ t
IN1972
0.23
0.22
0.22
0.24
0.26
0.34
0.39
0.43
0.45
0.47
0.43
0.14
0.15
0.16
0.15
0.16
0.14
0.12
0.11
0.12
0.13
0.18
0.14
0.14
0.15
0.14
0.14
0.13
0.13
0.13
0.13
0.12
0.12
0.50
0.49
0.48
0.46
0.44
0.39
0.36
0.33
0.31
0.28
0.27
0.50
0.48
0.46
0.43
0.39
0.35
0.31
0.29
0.26
0.23
0.22
NOTES: Dates of entry into a market are based on a sample for 1972–2002. IN1972 indicates banks that were already in the market in 1972,
for whom entry date cannot be determined.
TABLE 3
AVERAGE DEPOSIT MARKET SHARES BY
TIME IN THE MARKET
Time
Market share
(%)
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20+
3.2
3.3
3.5
3.5
3.6
3.4
3.0
3.1
3.2
3.0
3.2
3.5
3.2
3.3
3.4
3.5
3.8
3.8
3.5
3.5
7.1
NOTES: Dates of entry into a market are based on a sample
for 1972–2002, while the figures are based on the regression
sample for 1992–2002.
4. RESULTS
This section introduces the estimation results. The regressions include market and
year effects (318 and 11, respectively). We also introduce bank effects (over 7,000) in
790
:
MONEY, CREDIT AND BANKING
TABLE 4
ENTRANTS’ AVERAGE DEPOSIT MARKET SHARES BY THE ORDER AND METHOD OF ENTRY
Method of entry
Order of entry
0≤t <5
5 ≤ t < 10
10 ≤ t < 20
20 ≤ t †
Merger
De novo
Open branch
6.8
7.2
8.2
6.5
1.4
2.0
2.7
4.1
1.6
2.0
2.2
4.4
NOTES: In percentages. Dates of entry into a market are based on a sample for 1972–2002. † Entrants 20 ≤ t only include those that entered
after 1972.
TABLE 5
OLS REGRESSIONS OF MARKET SHARE ON THE ORDER OF ENTRY
Dependent variable:
Deposit market share
Explanatory variable
Entrant 0 ≤ t < 5
Entrant 5 ≤ t < 10
Entrant 10 ≤ t < 20
IN1972
(i)
(ii)
(iii)
−0.018
∗∗
(0.002)
−0.013
∗∗
(0.002)
−0.006
∗∗
(0.002)
0.040
∗∗
(0.002)
−0.041
∗∗
(0.002)
−0.030
∗∗
(0.002)
−0.014
∗∗
(0.002)
0.105
∗∗
(0.002)
NO
68,946
0.23
–
YES
68,946
0.69
∗∗
12.6
−0.050
∗∗
(0.007)
−0.037
∗∗
(0.007)
−0.019
∗∗
(0.005)
0.103
∗∗
(0.002)
−0.002
(0.002)
0.003
(0.001)∗
−0.008
(0.006)
0.002
(0.012)
YES
51,309
0.68
∗∗
10.0
Profit efficiency rank (t−1)
Cost efficiency rank (t−1)
Small-business loan ratio (t −1)
Personal income growth
Bank fixed effects
Observations
R-squared
F-stat
NOTES: All regressions include MSA and year fixed effects. Regression sample is for 1992–2002, with time of entry determined throughout
1972–2002. The dependent variable is the deposit market share for bank i in MSA market m in a given year. Standard errors are in
parentheses. ∗ Significant at 5%; ∗∗ Significant at 1%. F-statistic for the test that the bank fixed effects are jointly zero.
most regressions. Table 5 shows the results from estimating the deposit market share
of a bank in a market (in a given year) as a function of the order of entry of the bank
into that market, as described in expression (1). Column (i) is our base regression,
while column (ii) introduces bank fixed effects and column (iii) adds further timevarying bank and market controls. Based on the results from all the specifications,
the coefficients on all three of the defined order of entry periods are negative and
statistically significant at the 1% level of confidence (l.o.c.), indicating that entrants
ALLEN N. BERGER AND ASTRID A. DICK
:
791
have, on average, a lower market share than incumbents. 20 Moreover, this market
share disadvantage increases the more recent is the entrant. That is, later entrants do
worse than earlier entrants, so that a bank that has only been in the market for 4 years
or less has, on average, the lowest market share. Also, note that 1972 incumbents have
the highest market shares. Indeed, the biggest difference is between these incumbents
and all other banks. Lastly, note that our dependent variable is bounded between 0 and
1. However, when we estimate the model with a logit transformation of market shares
to allow the dependent variable to take on any real value, we find that our results are
robust. We keep the bounded dependent variable, however, for ease of interpretation
of the regression coefficients. 21
While the results are similar across the specifications in the column, we focus on
the results in column (ii), which incorporate individual-specific effects to control for
differences in market share across banks other than those reflected in our explanatory
variables. We do this since taking into account bank variation appears to be important.
In particular, when we break up the total sample variation into bank, market, and year
effects, we find that most of the variation in market share comes from variation
between banks (55%), followed by variation between markets (17%) and variation
between years (less than 1%). The R-squared increases from 23% to 69% when bank
fixed effects are included.
Thus, based on the results from column (ii), the most recent entrants have on
average market shares that are 4.1 percentage points lower than those of our base
case, post-1972 incumbents; entrants with 5–9 years in the market have lower shares
by 3.0 percentage points; and entrants with 10–19 years of market presence have
market shares that are 1.4 percentage points lower. Note that 1972 incumbents have,
on average, 10.5 percentage points higher market share than incumbents that entered
after 1972. This implies, for instance, that relative to 1972 incumbents, the most
recent entrants have almost 15 percentage points fewer in deposit market share. With
a regression fit close to 70%, the order of entry as well as bank, market, and year
effects appear to explain most of the variation in firms’ market shares.
Column (iii) incorporates to the specification in (ii) time-varying bank and market
controls. While bank fixed effects take into account unobserved individual characteristics, these are assumed to remain constant over time. However, we can think of certain
20. Note that the results are robust to using a continuous order of entry measure (with market share
increasing a quarter of a percentage point for every year in the market), as well as various other order of
entry definitions.
21. Clearly, analyzing the effect of time of entry on profits would be interesting, but as mentioned
earlier, there are data limitations to doing so. When we explore the link for single-market banks, however,
for whom profit data are available at the market level, we find that our results are similar to those obtained
using market share, at least for the newest entrants. Based on various specifications (controlling for bank,
year, and market effects), in particular, we find that the newest entrants have over 1 percentage point
lower profit rates (the mean/median of the distribution is 11%, based on net income over equity, and after
removing outliers). This result is significant at the 1% l.o.c. The other two coefficients of interest (entrants
from 5 to 9 years ago and entrants from 10 to 19 years ago) are also negative and decrease in magnitude,
but when we include enough controls (bank fixed effects, for instance), they become insignificant (results
not reported). We also find similar results using loan shares. These results do provide evidence of a link
between profits and market shares. However, given that they are based on single-market banks only, it is
difficult to make inferences more generally.
792
:
MONEY, CREDIT AND BANKING
bank characteristics that are likely to change over time and that can have a potential
effect on market share and on the decision of a bank to enter a market. In particular, we
introduce a proxy for a bank’s cost and profit X-efficiency, constructed as in Berger
and Mester (2003), as these could be important controls for bank quality. 22 Berger and
Mester (2003) find that these vary considerably during the 1990s, while DeYoung and
Hasan (1998) find that bank profit efficiency varies during the early years of de novo
banks’ lives. The results are robust to these additional controls. We also introduce the
ratio of small business loans to total loans as a way to control for the bank’s business focus (data available for 1993 onward). Since market share and these controls are likely
to be jointly endogenous, we lag these control variables and only use this specification
for sensitivity analysis. Lastly, we incorporate personal income growth to control for
a market characteristic that is likely to change over time. After we introduce these
controls, we find that the results are similar to those presented in column (ii). Note
that the specification is now based on the period 1994–2002 due to the limitations on
small business loan data, which implies a loss of close to 18,000 observations—yet the
results are very similar to those in column (ii). Note that the only control that appears
to matter is the cost efficiency rank, which indicates that a relatively more efficient
bank has a higher deposit market share, which is consistent with the efficient structure
hypothesis. 23
Does the method of entry affect the early-mover advantage?
We now explore whether the method of entry used by the entrant has an effect on the later-mover disadvantage we found earlier. The order of entry is interacted with two types of entry: by merger and by branch opening, leaving as the
base case the de novo entry method. As can be seen in column (i) of Table 6,
entering a market through a merger gives the bank a higher market share, since
the bank is buying up another bank’s existing branch network in the market. Entering by opening a branch, however, provides a lower share relative to de novo
banks.
Note that while a specification with bank fixed effects would be desirable given the
large variation across banks, it would not be appropriate in this exercise. This is due
to the selection of banks into a particular entry method and therefore the sources of
variation, which changes the interpretation of the within-bank regression coefficients.
Banks that enter by merger are large and geographically diversified compared to other
22. The main difference between standard financial ratios of cost and profit rates and the cost and
profit X-efficiency ranks we use is that the latter remove some of the differences in conditions facing
the individual banks. In particular, these ranks are based on the residuals from regressions of costs and
profits, respectively, on bank outputs (asset categories, off-balance sheet activities), fixed inputs (capital,
premises), and market prices for variable inputs (labor, deposits, purchased funds). The residuals are put
in rank order for each year and converted to a uniform scale over the [0,1] interval, where 0 is the least
efficient (highest cost or lowest profit) and 1 is the most efficient (lowest cost, highest profit), to make the
ranks comparable across years. Thus, a bank’s rank in a year is the proportion of sample banks with lower
efficiency (e.g., rank = 0.70 for a bank that is more efficient than 70% of the sample).
23. Under the efficient structure hypothesis, the largest players in a market will be those that are most
efficient, as efficiency is the mechanism thereby they grow and become large.
ALLEN N. BERGER AND ASTRID A. DICK
:
793
TABLE 6
DOES THE METHOD OF ENTRY AND GEOGRAPHIC DIVERSIFICATION MATTER?
Dependent variable:
Deposit market share
Explanatory variable
Entrant 0 ≤ t < 5
Entrant 5 ≤ t < 10
Entrant 10 ≤ t < 20
Entry by merger
Ent 0 ≤ t < 5 ∗ Merger
Ent 5 ≤ t < 10 ∗ Merger
Ent 10 ≤ t < 20 ∗ Merger
Entry by branch
Ent 0 ≤ t < 5 ∗ Open Branch
Ent 5 ≤ t < 10 ∗ Open Branch
Ent 10 ≤ t < 20 ∗ Open Branch
(i)
−0.032
∗∗
(0.002)
−0.021
∗∗
(0.002)
−0.013
∗∗
(0.002)
−0.004
(0.005)
0.036
∗∗
(0.006)
0.027
∗∗
(0.006)
0.027
∗∗
(0.006)
−0.038
∗∗
(0.004)
0.025
∗∗
(0.004)
0.020
∗∗
(0.004)
0.013
∗∗
(0.004)
Multimarket (10+)
Ent 0 ≤ t < 5 ∗ Multimkt (10+)
Ent 5 ≤ t < 10 ∗ Multimkt (10+)
Ent 10 ≤ t < 20 ∗ Multimkt (10+)
IN1972
Bank fixed effects
Observations
R-squared
0.032
∗∗
(0.002)
NO
68,946
0.26
(ii)
−0.022
∗∗
(0.002)
−0.012
∗∗
(0.002)
−0.002
∗∗
(0.002)
0.084
∗∗
(0.002)
−0.044
∗∗
(0.003)
−0.043
∗∗
(0.004)
−0.049
∗∗
(0.004)
0.040
∗∗
(0.002)
NO
68,946
0.26
NOTES: All regressions include MSA and year fixed effects. Regression sample is for 1992–2002, with time of entry determined throughout
1972–2002. The dependent variable is the deposit market share for bank i in MSA market m in a given year. The entry method base case is
de novo. Standard errors are in parentheses. ∗ Significant at 5%; ∗∗ significant at 1%.
banks, and they tend to choose the same method of entry every time they enter a new
market. Thus, the bank fixed effect cannot be distinguished from the entry method to
provide the interpretation that interests us here. Given the significance of the variation
across banks, the reported findings should be viewed with caution. 24
24. We also tried distinguishing “toeholds” from larger mergers. Indeed, we find that large mergers do
increase the market share of the entrant, whereas toehold acquisitions do not have such an effect. Again,
we caution about drawing conclusions from these findings because we have to drop the bank fixed effects.
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MONEY, CREDIT AND BANKING
In particular, the median asset size of a merger entrant is $1.5 billion compared
to $295 million of entrants via branch and $30 million of de novo entrants. Also,
94% of entrants by merger are multi-market banks. De novo entrants, however, are
virtually single-market banks by definition (with the exception of only 54 entrants
who entered more than one market the same year). Moreover, banks tend to stick to
the same entry method whenever they enter a new market. 25 Thus, the sources of
variation to identify the coefficients are different depending on the entry method (for
merger entrants, which are mostly multi-market, it is both the across-time and acrossmarket variation, while for de novo banks, it is only across-time). Thus, the bank
fixed effect would already control for bank quality and size, and with that implicitly
capture the effect of entry method.
Has “geographic diversification” an effect on the early-mover advantage?
We now explore whether large, geographically diversified entrants that offer extended branch networks outside the new market have a different experience from
smaller, more locally limited entrants. In particular, we define geographically diversified entrants to be those that have presence in at least 10 other metropolitan markets. 26
Such measure is correlated with the size of the branch network, the size of the bank
in terms of assets, and is also likely to be correlated to how much of a brand name
consumers ascribe to this bank—especially as branches themselves are believed to
be a form of bank advertising. 27
Column (ii) of Table 6 introduces interactions between the order of entry and the
indicator variable for geographic diversification, which takes on the value of 1 if
the bank operates in at least 10 other metropolitan markets. Indeed, geographically
diversified entrants have larger market shares than entrants that are not. Note that
here as well, the within regression is not the appropriate specification, since there is
virtually no variation within a bank across time in terms of whether it is geographically
diversified according to our definition (a bank is always one or the other). As a result,
the geographic diversification indicator and the bank fixed effect cannot be separated
from each other in a way as to provide us with the interpretation of interest.
Are investments in branch networks larger for early movers?
We now test whether “within-market” coverage, as measured by the size of the
branch network inside the market, plays a role. Indeed, an early entrant’s advantage
could be due to strategic investments in the branching network, which, ceteris paribus,
25. For instance, the entry-method Herfindahl within a bank (the sum of the squared proportions of
entry methods within a bank) is 0.7 for banks that enter more than one market, suggesting that most banks
use the same entry method each time they enter a market.
26. We chose this cutoff based on the distribution of the number of markets served across banks. In
the Appendix, we provide some statistics for the number of outside markets in which banks in our sample
operate. The mean is 5 markets, while the median is 0. The presence in at least 10 other markets happens
to be around the 90th percentile of the distribution, which is why we chose it for our definition. Trying
various cutoffs above 5 markets actually provide similar results, though the sharpest appear to be around
the current cutoff of 10 markets.
27. See Dick (2007) for further evidence on bank advertising.
ALLEN N. BERGER AND ASTRID A. DICK
:
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TABLE 7
INVESTMENTS IN BRANCH NETWORK
Dependent variable:
Branch density
Explanatory variable
Entrant 0 ≤ t < 5
Entrant 5 ≤ t < 10
Entrant 10 ≤ t < 20
IN1972
Bank fixed effects
Observations
R-squared
F-stat
(i)
−0.0068
∗∗
(0.0011)
−0.0067
∗∗
(0.0012)
−0.0030
∗∗
(0.0012)
0.0241
∗∗
(0.0011)
NO
68,946
0.37
–
(ii)
−0.0236
∗∗
(0.0011)
−0.0152
∗∗
(0.0011)
−0.0063
∗∗
(0.0010)
0.0466
∗∗
(0.0012)
YES
68,946
0.77
∗∗
15.4
NOTES: All regressions include MSA and year fixed effects. Regression sample is for 1992–2002, with time of entry determined throughout
1972–2002. The dependent variable is for bank i in MSA market m in a given year. Standard errors are in parentheses. ∗ Significant at 5%;
∗∗ significant at 1%. F-statistic for the test that the bank fixed effects are jointly zero.
might make it harder for later entrants to penetrate the market to the extent of the
incumbent. Table 7 shows the results from estimating a bank’s branch density in a
given market, measured as branches per square mile, as a function of our three orders
of entry variables and series of controls. The results are similar to our earlier ones
using deposit market share. In terms of magnitudes and from the bank fixed effects
regression, they indicate that the newest entrants have about half of the post-1972
incumbents’ branch network (our base case), while less recent entrants (5–9 years)
have about two-thirds, and the least recent entrants (10–19 years) have close to 90%
of the incumbents’ branch network. Interestingly, the 1972 incumbents, who have
the largest shares in the market, also have the largest rates of branch penetration. In
particular, they have almost double the size of the network of incumbents that came
in after 1972. Also note that this specification explains 77% of the variation in branch
networks among banks.
Is the early-mover advantage stable over time?
While the removal of geographic barriers to a bank’s expansion started in some U.S.
states as early as the 1970s, the final phase of deregulation took place during 1994–97
with the passage of the Riegle–Neal Interstate Banking and Branching Efficiency
Act in 1994, which allowed for nationwide branching. The regulatory framework in
place before these changes could have temporarily benefitted incumbents relative to
entrants, and as a result, our results could derive from such asymmetry. If this were
the case, we would expect that, following deregulation, any advantages originally
bestowed on incumbents should vanish over time. To explore this possibility we
split the sample into two time periods: 1992–97 and 1998–2002, before and during
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TABLE 8
IS THE EARLY-MOVER ADVANTAGE STABLE OVER TIME?
Dependent variable:
Deposit market share
Explanatory variable
Entrant 0 ≤ t < 5
Entrant 5 ≤ t < 10
Entrant 10 ≤ t < 20
IN1972
Bank fixed effects
Observations
R-squared
F-stat
1992–97
(i)
−0.034
∗∗
(0.003)
−0.022
∗∗
(0.003)
−0.006
∗∗
(0.002)
0.105
∗∗
(0.003)
YES
37,100
0.75
∗∗
12.8
1998–2002
(ii)
−0.064
∗∗
(0.003)
−0.051
∗∗
(0.003)
−0.027
∗∗
(0.003)
0.105
∗∗
(0.003)
YES
31,846
0.67
∗∗
13.3
NOTES. All regressions include MSA and year fixed effects. Regression sample is for 1992–2002, with time of entry determined throughout
1972–2002. The first (second) column is based on 1992–97 (1998–2002). The dependent variable is the deposit market share for bank i in
MSA market m in a given year. Standard errors are in parentheses. ∗ Significant at 5%; ∗∗ significant at 1%. F-statistic for the test that the
bank fixed effects are jointly zero. Moreover, another F-test rejects the equality of the order of entry coefficients across the two sample
periods at the 1% level of confidence (F = 95.9).
deregulation, and after, respectively. Table 8 shows that the early-mover advantage, if
anything, increases toward the later period, with sometimes a doubling in the market
share disadvantage for entrants. 28 An F-test rejects the null hypothesis of coefficient
stability over time (1% l.o.c.). One possible explanation for this result is that in the
latter period, during which banking markets are more deregulated and more entry is
observed (see Table 1), new entrants have to deal with more competitors also entering
those markets at the same time, and therefore have lower shares. Note that this result,
however, does not rule out the possibility that the earlier regulatory framework could
have permanently helped incumbents in developing their customer base more than
under a different regime.
4.1 Ruling Out Alternative Stories to the Early-Mover Advantage
While our tests up to this point establish that early entrants capture a larger share
of the deposits in a market, there remains the question of whether this correlation
is really the result of the strategic advantage of early movers. That this empirical
regularity exists is certainly a novel and striking finding. However, our sample is
one of survivors, and as such, it is biased, as failures are ignored. For instance, one
alternative story could be that of a learning model, where firms face a self-reinforcing
productivity shock every period and discover their type over time, as in Hopenhayn
28. Our results also hold for each year of the analysis separately, with the market share gap between
entrants and incumbents increasing slightly over the years.
ALLEN N. BERGER AND ASTRID A. DICK
:
797
(1992). In such a world, as a higher productivity shock today makes it more likely
that the productivity shock will be higher in the future, the model implies a stationary
equilibrium with a size distribution of firms that is stochastically increasing in the
age of firm cohorts. Similarly, under a scenario of imperfect capital markets, such
that most of the firm’s ability to invest and therefore grow are derived from internally
generated funds, the firm’s assets will be correlated with the firm’s age.
In order to address these issues, we carry the following test. In particular, we
explore whether earlier entrants have larger shares of the market compared to later
entrants, after surviving in the market for the same number of years. That is, we
explicitly account for the order in which entry has occurred by holding the number
of years since entry constant across all banks. To do this, we estimate market share
as a function of two main variables: (i) “order of entry,” which ranges from 1 to 30,
based on whether the bank entered the market first, second, third, and so on, over
our 30-year data; and (ii) the “number of years in the market,” which holds the time
since entry constant. Thus, we can make use in estimation of almost our entire data,
by pooling observations for the period 1973–2002. Note that we drop the year 1972,
that is, all the “1972 incumbents,” since for these banks we cannot determine the year
of entry (whether it was in 1972 or before that). This makes it a stringent test, since
it is based only on the sample of banks that entered after 1972, and not on the 1972
incumbents which appear to be a main force behind our earlier results. Moreover, this
sample includes many banks that failed that are no longer in the regression sample
used in the previous analysis. Also note that unlike our previous exercise, the group
of first entrants, for instance, is made up of banks that have been in the market from
1 to 30 years.
Table 9 shows results for this test. The first column in Panel A of the table shows
that the higher the order of entry in which the bank entered the market, the lower its
market share is relative to banks with a lower order of entry. Based on the coefficient
on the “order of entry,” we find that a bank loses 0.1 percentage points for each change
in its order of entry into the market from nth to nth+1 (or for each year that the bank
delays entry), which is a 1 percentage point per decade of delayed entry. Moreover,
each additional year in the market increases a bank’s deposit market share by 0.01
percentage points, regardless of the order in which the bank entered the market—
according to the coefficient on “ number of years in the market.” Thus, this test is
particularly powerful as it allows us to separate the effects of market tenure from
those of the order of entry. The test indicates that while market tenure increases a
bank’s market share, the later a bank enters a market, the lower its market share is
relative to early entrants. The results are similar if we use branch density instead of
market share (results not shown).
Column (ii) in Panel A is similar, though instead of allowing the order of entry to
be continuous, it divides the order of entry into six different categories, as follows:
(i) first to fifth entrants, (ii) sixth to tenth, (iii) eleventh to fifteenth, (iv) sixteenth
to twentieth, (v) twenty-first to twenty-fifth, (vi) twenty-sixth to thirtieth. Thus, if
an early-mover advantage exists, we would expect the first group (of first, second,
third, fourth, and fifth entrants) to have higher market shares than the second group,
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TABLE 9
FURTHER TESTS OF THE EARLY-MOVER ADVANTAGE
Panel A
Dependent variable:
Deposit market share
Sample: 1973–2002
Explanatory variable
Order of entry
(i)
(ii)
−0.001
∗∗
(0.000)
1st to 5th entrant
6th to 10th entrant
11th to 15th entrant
16th to 20th entrant
21st to 25th entrant
No. of yrs. in the market
Bank fixed effects
Observations
R-squared
F-stat
0.001
∗∗
(0.000)
YES
81,649
0.60
∗∗
10.1
0.030
∗∗
(0.005)
0.028
∗∗
(0.004)
0.024
∗∗
(0.003)
0.022
∗∗
(0.002)
0.010
∗∗
(0.001)
0.001
∗∗
(0.000)
YES
81,649
0.60
∗∗
10.1
Panel B
Dependent variable:
Deposit market share
Sample: 1992–2002
Inc.+entrant multi-mkt. banks
(i)
Entrant 0 ≤ t < 5
Entrant 5 ≤ t < 10
Entrant 10 ≤ t < 20
IN1972
Bank fixed effects
Observations
R-squared
F-stat
−0.045
∗∗
(0.004)
−0.034
∗∗
(0.005)
−0.021
∗∗
(0.005)
0.098
∗∗
(0.005)
NO
14,626
0.35
(ii)
−0.069
∗∗
(0.004)
−0.062
∗∗
(0.005)
−0.048
∗∗
(0.005)
0.098
∗∗
(0.005)
YES
14,626
0.54
∗∗
9.48
NOTES: All regressions include MSA and year fixed effects. The dependent variable is the deposit market share for bank i in MSA market
m in a given year. Standard errors are in parentheses. ∗ Significant at 5%; ∗∗ significant at 1%. F-statistic for the test that the bank fixed
effects are jointly zero. In Panel A, the base case is the 26th to 30th entrant group, and the sample excludes 1972 incumbents. In Panel B, the
regression sample is based on banks that are both incumbent in at least one market and entrant in at least one other market. While the sample
covers 1992–2002, time of entry is determined throughout 1972–2002.
ALLEN N. BERGER AND ASTRID A. DICK
:
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and this group, in turn, should have higher market shares than the next, and so on.
Since we let our base case be the last group (vi) in the regression, we expect all the
coefficients to be positive and in decreasing magnitude. Indeed, this is what we find.
In particular, the first group of entrants has 3.0 percentage points higher market share
than the base group, followed by a 2.8 percentage points advantage for the second
group, 2.4 percentage points for the third group, 2.2 percentage points for the fourth
group, and 1.0 percentage point for the fifth group. Each additional year in the market,
as before, increases market share by 0.1 percentage points.
We also offer another test for whether our earlier results are related to a strategic
advantage of early entry. If imperfect capital markets or learning models are the main
explanation for our earlier results, we should find that multi-market banks that are
incumbents in some markets but entrants in others, do not depict large differences in
market shares across these markets. Why? Because if the bank wants to enter and grow
in a new market, and the alternative stories above apply, the bank will have access to
its internal capital markets and firm-level knowledge in order to do so—thus, if it does
not grow to be like the incumbent, there has to be another reason besides availability
of capital and/or accumulated firm learning. Indeed, the latter is what we find. On the
contrary, larger, multi-market banks do not achieve in new markets the large market
shares that they have in markets where they are incumbents. That is, even these bank
entrants do worse relative to incumbents.
To do this, we re-estimate our earlier model on a subsample of banks that are
both incumbent in at least one market and entrant in at least one other market. This
subsample is made up of larger, more geographically diversified banks than the overall
sample. The results are shown in Panel B of Table 9. The specifications are like the
ones presented in Table 5, columns (i) and (ii), with the difference that now the
estimation is based on a subsample that is one-fifth of the original sample. Yet the
results are strikingly similar, and if anything, the later-mover disadvantage appears
to be actually stronger for these banks. If the above alternative stories were behind
our earlier results, we should find no difference between incumbents and these larger
entrants, since both are likely to have similar access to funds when they want to grow. 29
In fact, compared to the overall sample, the results indicate that the difference between
these banks’ market shares when they are entrants and those when they reach maturity
in the market are larger. This suggests that these banks grow faster than smaller, less
geographically diversified banks. Note that similar results are obtained here if we use
branch density instead of market share (results not shown).
These results are particularly enlightening because while there could be some
market level learning that is relevant to a bank considering entry and growth in a
29. As mentioned earlier, when a bank is acquired, it becomes affiliated to a MBHC, which might
serve as an internal capital market for its member institutions (Houston, James, and Marcus 1997, Houston
and James 1998, Campello 2002). When we control for whether the bank is subjected to an acquisition
(using the specification in Table 9, Panel B, column (i)), we find that our results are unchanged, and that
the coefficient on the indicator variable for whether the bank is acquired (which takes on the value of 1 if
it is acquired and 0 otherwise) is positive and significant, as we would expect, though contributing only
0.6 percentage points to market share. While our reported results do not separately show this effect, our
inclusion of bank fixed effects should already account for this.
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MONEY, CREDIT AND BANKING
market, its access to capital is likely to play a larger role. For example, a bank such
as Bank of America already knows a lot about banking. When this bank is deciding
whether to enter new markets, the bank researches which markets offer the most
profitable opportunities for entry. Thus, there is little learning left to do about these
new markets. However, with its “deep pockets” Bank of America can come in and
build a large branch network to drive out smaller banks if it wanted to—that is, if
it believes it would be profitable to do so as it would be able to attract other banks’
customers. The evidence in this paper shows that there is a fraction of consumers that
will stay with the incumbent regardless, and that is why a bank like Bank of America
might not become as large in markets where it enters later, and branch out in these as
much.
Does the market turnover in population have an effect on the early-mover advantage?
While there are several factors giving rise to an early-mover advantage, consumer
switching costs are likely to be important in banking markets, where repeated transactions and relationship building is central to banking services. The theory on consumer switching costs suggests a relationship between the degree of market power
over a firm’s consumers and a market’s growth and turnover in its customer base.
In an infinite-period two-firm market with consumer switching costs and customer
turnover, Beggs and Klemperer (1992) provide a proposition that states that prices
(and profits) are decreasing in the rate of growth in the market, due to the reduction
in the proportion of locked-in consumers relative to new market consumers and the
greater importance of the future stream of profits relative to current profits. Similarly,
in a two-firm, two-period model, Klemperer (1987) finds that the greater the share
of unattached consumers in the second period, the more sensitive consumers are to
price differences among the firms and the more competitive the market. Intuitively,
the incumbent is likely to have a large degree of market power over its current customer base. In the face of entry, the incumbent confronts two types of consumers,
those already captive and the rest of the consumers in the market, over which both the
incumbent and the entrant fight. When population growth is high, the latter consumer
type grows, thus providing the entrant with a more similar playing field. As mentioned
earlier, Sharpe (1997) tests these predictions with banking data and finds that greater
customer flows into banking markets are associated with higher, more competitive
deposit interest rates. In lending markets, the model in Marquez (2002) predicts that
entry will be easier in markets with a high degree of borrower turnover, given that
incumbents enjoy an information advantage over entrants as they obtain proprietary
information through the lending process.
Similarly in our current setup, if consumer switching costs are a significant element
behind our findings, we should expect the early-mover advantage to be stronger in
markets with low population growth and weaker in markets with high population
growth. As new, potential consumers move into the market, a smaller proportion of
old consumers are locked in with a given bank, and therefore the advantage of having
ALLEN N. BERGER AND ASTRID A. DICK
:
801
TABLE 10
ARE HIGH VS. LOW POPULATION GROWTH MARKETS DIFFERENT?
Dependent variable:
Deposit market share
Explanatory variable
Entrant 0 ≤ t < 5
Entrant 5 ≤ t < 10
Entrant 10 ≤ t < 20
IN1972
Bank fixed effects
Observations
R-squared
F-stat
High growth
(i)
−0.067
∗∗
(0.003)
−0.047
∗∗
(0.003)
−0.028
∗∗
(0.002)
0.047
∗∗
(0.003)
YES
18,864
0.72
∗∗
12.73
Low growth
(ii)
0.001
(0.004)
0.001
(0.004)
0.003
(0.003)
0.180
∗∗
(0.004)
YES
16,394
0.75
∗∗
11.54
NOTES: All regressions include MSA and year fixed effects. Regression sample is for 1992–2002, with time of entry determined throughout
1972–2002. The first (second) column is based on the top (bottom) quartile of markets from the distribution of average population growth
over 1972–2002. The dependent variable is the deposit market share for bank i in MSA market m in a given year. Standard errors are
in parentheses. ∗ Significant at 5%; ∗∗ significant at 1%. F-statistic for the test that the bank fixed effects are jointly zero. Moreover,
another F-test rejects the equality of the order of entry coefficients across the two samples at the 1% level of confidence [F(3, 31624) = 86.97].
entered that market earlier should diminish, given that both new and old market banks
should be perceived more or less equally by new consumers (controlling for other
firm characteristics).
To test for this possibility, we construct two groups of markets, with high and low
population growth. Taking the average of the annual market growth rates over our
sample period (1972–2002) for each market, a high (low) growth market is defined
as the top (bottom) quartile of the distribution of markets. Re-estimating our specifications for each group, we find that, indeed, markets with high population growth
present lower barriers to entry relative to low growth markets (see Table 10). Particularly interesting is the fact that there is no difference between recent entrants and
those that entered after 1972, but rather, the market share difference is between 1972
incumbents and all other market banks—and this difference is large. In particular, we
find that while in high growth markets the difference between 1972 incumbents and
those incumbents that entered later is of around 5 percentage points, in low growth
markets this difference is 18 percentage points. Moreover, this later-mover disadvantage is the same across all groups of banks other than the 1972 incumbents. In
high growth markets, conversely, even the difference between the newest entrants
and 1972 incumbents is no more than 11 percentage points. An F-test rejects the
null hypothesis that the order of entry coefficients are equal across the two samples (1% l.o.c.). These results support the prediction from consumer switching costs
models that higher population growth markets should exhibit less of an early-mover
advantage. Indeed, they suggest that consumer switching costs are likely an important
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factor behind the documented market share difference. 30 In low growth markets, the
number of new consumers is low, and therefore entrants should face greater barriers
to their market growth.
4.2 Discussion
Our results show a robust empirical relationship between a bank’s order of entry
and its predominance in the market. If banks were all identical, we would unambiguously expect later entrants to have lower market shares, on average, than those
of earlier entrants. However, banks are not identical. It is not uncommon in banking markets to find some of the largest US banks with branch networks across
the country coexisting with small, single branch local banks. Moreover, there is
large variation in terms of the order of entry within each bank category, with national brand-name banks entering markets along side with small de novo firms every
year.
When we introduce the fact that banking firms are differentiated, we get a more
nuanced story of bank entry. Measuring bank differentiation in terms of geographic
diversification, for instance, we learn that even entrants can attain a significant market presence if they offer large branch networks, thus reducing the disadvantage of
entering later.
Moreover, our results suggest a similar situation for incumbents, where their market
position is also related with what they have to offer to their clients. In particular,
incumbents are not guaranteed market dominance by the mere fact of having entered
early. While that certainly is part of the story behind the distribution of firms’ market
shares in banking markets, incumbents that achieve and maintain a large market
presence appear to do so by offering a higher quality service as suggested by the
different results for branded and unbranded incumbents. As shown in Table 11, early
movers also tend to have a greater branch density relative to entrants (44% higher),
and are almost twice the age of newer entrants—where age could be interpreted as a
proxy for bank experience.
In terms of geographic diversification, an interesting pattern arises over the period. While in 1992 incumbents were more geographically diversified than entrants,
toward the latter part of our sample, many new entrants tend to be larger and more
geographically diversified than incumbents, with new entrants having on average
presence on four states, versus incumbents with presence on two states (though the
medians of the distribution are the same). This result is driven by some national
banks in the tail of the distribution, that have operations in dozens of states and
continue to move into new markets. Following nationwide branching deregulation,
30. This test is particularly useful to disentangle the factors behind the early-mover advantage that we
find. For instance, early movers could simply be higher quality providers, who upon entering the market
captured the prime location in town, for instance. The fact that population turnover appears to significantly
matter for our results is indication that consumer switching costs are relatively important.
ALLEN N. BERGER AND ASTRID A. DICK
:
803
TABLE 11
AVERAGE BANK CHARACTERISTICS BY ORDER OF ENTRY INTO THE MARKET
Bank characteristics
0≤t <5
5 ≤ t < 10
10 ≤ t < 20
20 ≤ t
Gross total
assets
No. of
states
27.2B
9.1B
4.1B
5.8B
2.85
1.54
1.22
1.20
No. of outside
markets
11.27
4.38
2.31
1.92
Branch
density
Age
Top bank
(%)
0.00453
0.00364
0.00403
0.00605
58
43
36
76
14.3
6.3
7.1
72.3
NOTES: Dates of entry into a market are based on a sample for 1972–2002. The last column shows the breakup of banks with the largest
market share in the market among the four order of entry categories.
banks had the opportunity to expand to new markets across the country, and in
doing so become higher quality banks offering the convenience of large branch and
ATM networks. Many market incumbents have taken advantage of this opportunity and some have by now become regional or even national banks, sometimes
joining the group of geographically diversified banks that are buying up market
leaders and incumbents in other markets. Other incumbents, however, have chosen to stay put solely with operations in the home market, and have sometimes
been recent targets of mergers or face increasing competition from new banks.
However, their early-mover advantage aids them in retaining some of their market
leadership.
The market dominance of early movers can be appreciated on the last column
in Table 11. When we rank banks in each market by deposit shares, we find that
about three quarters of the banks with the largest share in the market are banks that
entered over 20 years ago, reflecting once again the strong relationship between market
leadership and the order of entry. Incumbent’s market shares are also ranked much
higher, on average, than those of later entrants. Following deregulation, however,
banks have had the opportunity to enter new markets, and the evidence indicates that
large, geographically diversified banks that enter new markets through a merger can
also attain market leadership relatively quickly. As a result, this kind of later entrants
do not suffer as much from the early-mover advantage. For instance, while in 1992,
87% of top market banks are incumbents, in 2002 their share in market leadership
decreases to 57%. An interesting question that arises from this is how consumers
are affected by these changes. On the one hand, the anecdotal evidence suggests
that consumers tend to be disrupted whenever their current bank is bought by a new
one, and moreover, it is not clear what the competitive effects from mergers are. On
the other hand, many of the buyers in the large mergers are big national brands and
geographically diversified banks that might provide as a result a higher quality service
to consumers, at least over time. Table 12 shows the preferred method of entry by
the order of entry and bank size. Traditionally, large banks have preferred mergers
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:
MONEY, CREDIT AND BANKING
TABLE 12
PREFERRED METHOD OF ENTRY INTO A MARKET BY BANK SIZE AND ORDER OF ENTRY
Order of entry
Assets
10B+
1B–10B
300M–1B
100M–300M
100M and less
0≤t <5
5 ≤ t < 10
10 ≤ t < 20
20 ≤ t †
Merger
Merger
Open branch
Open branch
De novo
Merger
Merger
Open branch
De novo
De novo
Merger
Merger
De novo
De novo
De novo
Merger
Open branch
De novo
De novo
De novo
NOTES: Dates of entry into a market are based on a sample for 1972–2002. † Entrants 20 ≤ t only include those that entered after 1972. Bank
size is measured by gross total assets, adjusted by 1994 dollars.
while small banks have preferred de novo (results in Table 12 are stable over the
period).
The specific case of Bank of America, for example, which is one of the largest
retail banks, is useful. Bank of America entered over 100 markets in the 1990s,
mostly by merger, such that it was usually able to obtain a big stake in the market
right after entering. However, compared to markets where it is an incumbent (usually
with presence since 1972 or before), the deposit market shares of Bank of America,
as well as its branch networks, are lower in markets where it is an entrant—especially
a recent one.
5. CONCLUDING REMARKS
The entry literature provides an abundance of work, some of which has emphasized the advantages of entering early into a market. Empirical research documenting the effects of early entry, however, is not as abundant, and has focused on differentiated goods where innovation is central to the industry. In contrast, this paper explores the existence of an early-mover advantage in the service industry of
banking.
Using a unique data set for 1972–2002 on all banks in all urban markets in the
United States, this paper analyzes whether there are differences in deposit market
shares among banking firms in a given market based on how early they entered. The
results indicate that later entrants have lower market shares, on average, controlling
for bank, market and year effects.
While identifying the factors driving the early mover advantage documented here
is outside the scope of this paper, our measure of the market share advantage of early
entry may be interpreted as an indirect measure of consumer switching costs and thus
of barriers to entry in the industry, which can be useful for assessing the effect of
competition policy.
ALLEN N. BERGER AND ASTRID A. DICK
:
805
APPENDIX: SUMMARY STATISTICS, 1992–2002
Variable
Gross total assets ($000)
Deposit market share
Entrant 0 ≤ t < 5
Entrant 5 ≤ t < 10
Entrant 10 ≤ t < 20
Entrant 20 ≤ t
IN1972
Entry by merger
Entry by branch opening
Entry by de novo
Number of outside markets
Branded
Market per capita income
Market annual population change
Market population
Number of observations (bank–market–year)
Mean
St. dev.
Min
Max
12,400,000
0.049
0.301
0.142
0.146
0.412
0.371
0.165
0.215
0.255
5
0.114
25,763
0.013
1,559,255
69,337
55,100,000
0.093
0.458
0.349
0.353
0.492
0.483
0.371
0.411
0.436
16
0.318
6,011
0.011
2,030,660
107
0.000
0
0
0
0
0
0
0
0
0
0
10,253
−0.050
56,780
543,000,000
0.931
1
1
1
1
1
1
1
1
140
1
60,839
0.085
9,677,220
NOTE: Constructed on the basis of the FDIC Summary of Deposits; Federal Reserve Report on Condition and Income; U.S. Census; Bureau
of Economic Analysis.
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